The task of single-step retrosynthesis prediction is treated as a supervised learning task, commonly categorized as template-based or template-free [1]}. Template-based approaches use manually curated or data-driven reaction templates [2]}. These templates represent the general atoms and bond structures required to perform a reaction. Therefore the objective is to predict the most appropriate template to break down the molecule [3]}, [4]}. Template-free approaches are treated as a sequence prediction problem predicting one token of a chemical SMILES vocabulary at a time [5]}, [6]}, drawing inspiration from natural language processing [7]}. Recently variations of these two approaches have emerged. In semi-template-based [8]}, [9]}, a molecule is first broken down into subparts, and then the subparts are rebuilt into chemically viable reactants. Lastly, though many models leverage the sequence-based SMILES notation, there are also attempts to utilize graph-based descriptors across these approaches, exploiting the advantages of a molecular graph [4]}, [11]}.
